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Variable selection in statistical modeling can differ significantly between methods. Triangulating results across multiple approaches enhances data interpretation and confidence in identifying key variables.

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Area of Science:

  • * Statistical modeling
  • * Machine learning
  • * Data science

Background:

  • * Variable selection is challenging with many variables and multicollinearity.
  • * Existing methods may yield inconsistent results, impacting inference and reproducibility.
  • * Evaluating the impact of different statistical approaches on variable selection is crucial.

Purpose of the Study:

  • * To assess how variable selection outcomes vary across different statistical methods.
  • * To investigate if combining results from multiple methods (triangulation) improves data interpretation.
  • * To determine the reliability and stability of variable selection techniques.

Main Methods:

  • * Utilized a real-world dataset with 408 subjects and 337 explanatory variables.
  • * Employed ten automated variable selection methods with optimized hyperparameters.
  • * Evaluated variable selection results based on cross-validation error minimization.

Main Results:

  • * Ten automated variable selection methods produced substantially different results.
  • * Variable selection outcomes and model sparsity varied greatly among methods.
  • * Two consistently selected variables explained most of the outcome's variability, indicating high importance.

Conclusions:

  • * Triangulation of variable selection results across multiple methods enhances data interpretation.
  • * Evaluating covariate stability across methods increases confidence in identifying important variables.
  • * Combining diverse statistical approaches offers robust insights in inferential modeling.